An Evaluation and Selection of 3G Mobile Value-Added Service

As the wireless communication and mobile phone market develop rapidly, telecommunication dealers provide diverse mobile value-added services for consumers to choose from. However, which mobile value-added services are those consumers need have become a worthy issue for discussion. In this empirical study, cluster analyses and analytic hierarchy processes are used to investigate and understand the need for cognition in the young users (20-29 years old). The selected subjects’ preferences for services, like mobile communication service, mobile entertainment service, mobile information service and mobile transaction service are evaluated. By surveying the subjects’ need for recognition, cluster analysis can further be used to cluster diverse mobile value-added services. Furthermore, by means of the Analytic Hierarchy Process (AHP), services that subjects pay more attention to can be sifted out for the further development of service functions. The results of analysis indicate that the mobile value-added services young users pay most attention to are: wireless emergency services in the communications category, mobile mapping in the information category, mobile taxi services in the communication category, contact list in the communication category and short messaging service in the communications category

Developing a SCORM-based U-Learning LMS System

An integrated content and learning management system (LMSs) which has the characteristic of being ubiquitous is the most essential component of U-Learning. However, most modern learning management systems have different architectures, which makes it’s difficult to integrate the numerous learning resources, and reusability is hard to achieve. Otherwise, most learning resources read on mobile platforms are still restricted to electric books or digital learning materials. So, it’s not easy to manage the learning progress and immediately information providing or interactions between learners and instructors are impossible. A research is proposed in this paper to develop a SCORM compliant blended U-Learning LMS system, which emphasizes the content compilation, content packaging and the implement of SCORM run-time environment to have learning materials being reusable and interoperable

Investment by Chinese construction firms in the UK infrastructure sector: volumes, patterns and trends

The expansion of global construction markets has substantial implications for companies engaged in cross-border transactions and financing. The convergence of the UK and the People’s Republic of China (PRC) infrastructure policy agendas has led to significant investment from Chinese construction companies in the UK infrastructure sector. On the demand side, the UK Government’s National Infrastructure Plan sets out a wide range of investment priorities. The PRC Government’s One Belt, One Road strategy, on the supply side, has prioritized outward foreign investment into Eurasia. The international business literature on foreign market entry with a specific organizational capability perspective is drawn upon to understand the rationale for Chinese construction companies to invest in the UK infrastructure market. Two Chinese construction multinational companies currently engaging in UK infrastructure projects are studied through interpreting secondary sources. Findings indicate that their pursuit of hybrid market entry modes are underpinned by corresponding hybrid capability exploitation and acquisition motivations

Solving multiple-criteria R&D project selection problems with a data-driven evidential reasoning rule

In this paper, a likelihood based evidence acquisition approach is proposed to acquire evidence from experts'assessments as recorded in historical datasets. Then a data-driven evidential reasoning rule based model is introduced to R&D project selection process by combining multiple pieces of evidence with different weights and reliabilities. As a result, the total belief degrees and the overall performance can be generated for ranking and selecting projects. Finally, a case study on the R&D project selection for the National Science Foundation of China is conducted to show the effectiveness of the proposed model. The data-driven evidential reasoning rule based model for project evaluation and selection (1) utilizes experimental data to represent experts' assessments by using belief distributions over the set of final funding outcomes, and through this historic statistics it helps experts and applicants to understand the funding probability to a given assessment grade, (2) implies the mapping relationships between the evaluation grades and the final funding outcomes by using historical data, and (3) provides a way to make fair decisions by taking experts' reliabilities into account. In the data-driven evidential reasoning rule based model, experts play different roles in accordance with their reliabilities which are determined by their previous review track records, and the selection process is made interpretable and fairer. The newly proposed model reduces the time-consuming panel review work for both managers and experts, and significantly improves the efficiency and quality of project selection process. Although the model is demonstrated for project selection in the NSFC, it can be generalized to other funding agencies or industries.Comment: 20 pages, forthcoming in International Journal of Project Management (2019

Finding minimum gene subsets with heuristic breadth-first search algorithm for robust tumor classification

Background: Previous studies on tumor classification based on gene expression profiles suggest that gene selection plays a key role in improving the classification performance. Moreover, finding important tumor-related genes with the highest accuracy is a very important task because these genes might serve as tumor biomarkers, which is of great benefit to not only tumor molecular diagnosis but also drug development. Results: This paper proposes a novel gene selection method with rich biomedical meaning based on Heuristic Breadth-first Search Algorithm (HBSA) to find as many optimal gene subsets as possible. Due to the curse of dimensionality, this type of method could suffer from over-fitting and selection bias problems. To address these potential problems, a HBSA-based ensemble classifier is constructed using majority voting strategy from individual classifiers constructed by the selected gene subsets, and a novel HBSA-based gene ranking method is designed to find important tumor-related genes by measuring the significance of genes using their occurrence frequencies in the selected gene subsets. The experimental results on nine tumor datasets including three pairs of cross-platform datasets indicate that the proposed method can not only obtain better generalization performance but also find many important tumor-related genes. Conclusions: It is found that the frequencies of the selected genes follow a power-law distribution, indicating that only a few top-ranked genes can be used as potential diagnosis biomarkers. Moreover, the top-ranked genes leading to very high prediction accuracy are closely related to specific tumor subtype and even hub genes. Compared with other related methods, the proposed method can achieve higher prediction accuracy with fewer genes. Moreover, they are further justified by analyzing the top-ranked genes in the context of individual gene function, biological pathway, and protein-protein interaction network. Keywords: Gene expression profiles; Gene selection; Tumor classification; Heuristic breadth-first search; Power-law distributio

Determination of Outflow Properties for the Quasi-thermal Radiation-Dominated Gamma-Ray Bursts

Some gamma-ray bursts (GRBs) are observed with prompt phase changing from quasi-thermal to non-thermal emission. The quasi-thermal emission is always well described by a multi-color blackbody function, and based on this modeling, a characteristic temperature with corresponding flux is taken as a probe to diagnose the magnetization properties of the central engine with the `top-down' approach proposed by Gao \& Zhang. Furthermore, the initial radius of the acceleration for the outflow as well as the magnetization parameter ($\sigma_0$) could be constrained to within a narrower range than those with a pair of temperature and flux from modeling with a single blackbody plus an empirical function (BAND function or exponential cut-off power law). We apply this method to some bursts with known emission properties, such as GRB 210121A from a typical pure hot fireball and GRB 210610B from a hybrid jet. It is found that this method works well on these control samples. With this method, we find it is suggestive that the photospheric emission of GRB 221022B is mainly from a fireball, rather than from a hybrid jet, while the non-thermal component is caused by internal shock (IS) mechanism, due to the increasing Lorentz Factor with time.Comment: 9 pages, 3 figures, submitted to ApJ on 06-May-202

Finding minimum gene subsets with heuristic breadth-first search algorithm for robust tumor classification

Background: Previous studies on tumor classification based on gene expression profiles suggest that gene selection plays a key role in improving the classification performance. Moreover, finding important tumor-related genes with the highest accuracy is a very important task because these genes might serve as tumor biomarkers, which is of great benefit to not only tumor molecular diagnosis but also drug development. Results: This paper proposes a novel gene selection method with rich biomedical meaning based on Heuristic Breadth-first Search Algorithm (HBSA) to find as many optimal gene subsets as possible. Due to the curse of dimensionality, this type of method could suffer from over-fitting and selection bias problems. To address these potential problems, a HBSA-based ensemble classifier is constructed using majority voting strategy from individual classifiers constructed by the selected gene subsets, and a novel HBSA-based gene ranking method is designed to find important tumor-related genes by measuring the significance of genes using their occurrence frequencies in the selected gene subsets. The experimental results on nine tumor datasets including three pairs of cross-platform datasets indicate that the proposed method can not only obtain better generalization performance but also find many important tumor-related genes. Conclusions: It is found that the frequencies of the selected genes follow a power-law distribution, indicating that only a few top-ranked genes can be used as potential diagnosis biomarkers. Moreover, the top-ranked genes leading to very high prediction accuracy are closely related to specific tumor subtype and even hub genes. Compared with other related methods, the proposed method can achieve higher prediction accuracy with fewer genes. Moreover, they are further justified by analyzing the top-ranked genes in the context of individual gene function, biological pathway, and protein-protein interaction network. Keywords: Gene expression profiles; Gene selection; Tumor classification; Heuristic breadth-first search; Power-law distributio